# Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks

arXiv: https://arxiv.org/abs/2412.10570 · HTML: https://mlsanity.com/paper/2412.10570/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Max-value Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/1703.01968/) (2017)
- [A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement](https://mlsanity.com/paper/2204.09308/) (2022)
- [Epistemic Uncertainty Sampling](https://mlsanity.com/paper/1909.00218/) (2019)
- [Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal](https://mlsanity.com/paper/2303.01560/) (2023)
- [One step closer to unbiased aleatoric uncertainty estimation](https://mlsanity.com/paper/2312.10469/) (2023)
- [PI3NN: Out-of-distribution-aware prediction intervals from three neural networks](https://mlsanity.com/paper/2108.02327/) (2021)
- [Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation](https://mlsanity.com/paper/2212.06370/) (2022)
- [Univariate Skeleton Prediction in Multivariate Systems Using Transformers](https://mlsanity.com/paper/2406.17834/) (2024)

## Built on by (most cited first, 3 of 3)

- [Epistemic Wrapping for Uncertainty Quantification](https://mlsanity.com/paper/2505.02277/) (2025)
- [A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection](https://mlsanity.com/paper/2604.01798/) (2026)
- [Learning Parametric Nitrogen Fertilizer Response Curves Using Neuro Symbolic Regression](https://mlsanity.com/paper/2605.31276/) (2026)

## Abstract (arXiv)

Obtaining high certainty in predictive models is crucial for making informed and trustworthy decisions in many scientific and engineering domains. However, extensive experimentation required for model accuracy can be both costly and time-consuming. This paper presents an adaptive sampling approach designed to reduce epistemic uncertainty in predictive models. Our primary contribution is the development of a metric that estimates potential epistemic uncertainty leveraging prediction interval-generation neural networks. This estimation relies on the distance between the predicted upper and lower bounds and the observed data at the tested positions and their neighboring points. Our second contribution is the proposal of a batch sampling strategy based on Gaussian processes (GPs). A GP is used as a surrogate model of the networks trained at each iteration of the adaptive sampling process. Using this GP, we design an acquisition function that selects a combination of sampling locations to maximize the reduction of epistemic uncertainty across the domain. We test our approach on three unidimensional synthetic problems and a multi-dimensional dataset based on an agricultural field for selecting experimental fertilizer rates. The results demonstrate that our method consistently converges faster to minimum epistemic uncertainty levels compared to Normalizing Flows Ensembles, MC-Dropout, and simple GPs.